Overlapping Patterns Recognition with Linear and Non-Linear Separations Using Positive Definite Kernels
The detection of overlapping patterns in unlabeled data sets referred as overlapping clustering is an important issue in data mining. In real life applications, overlapping clustering algorithm should be able to detect clusters with linear and non-linear separations between clusters. The authors propose in this paper an overlapping clustering method based k-means algorithm using positive definite kernel. The proposed method is well adapted for clustering multi label data with linear and non linear separations between clusters. Experiments, performed on overlapping data sets, show the ability of the proposed method to detect clusters with complex and non linear boundaries. Empirical results obtained with the proposed method outperform existing overlapping methods.